ENMG reports depended on manual extraction from medical PDFs, causing slow turnaround and inconsistent report quality.

Built an end-to-end platform that converts exam PDFs to structured JSON, applies white-box QA rules, and generates standardized final PDF reports.

Python pipeline with multilingual extraction (PT/EN), anomaly flags, reference-based analysis logic, and a desktop .exe app for non-technical users.

Veterinary operations had connectivity gaps and manual workflows, causing data loss risk and frequent rework in field routines.

Engineered a mission-critical mobile app with offline-first architecture, custom sync engine (SQLite + Supabase), and automated operational workflows.

Expo/React Native + SQLite for local durability, Supabase for cloud sync, server-wins conflict resolution, tombstones soft-delete, and WhatsApp-native reporting.

Agricultural specialty teams faced fragmented and expensive data across patents, regulations, and scientific repositories, delaying innovation by months.

Built a competitive intelligence platform that consolidates public/private sources and transforms raw data into strategic dashboards and predictive insights.

Vue + Laravel stack with ETL pipelines, API integrations (MAPA, Comex Stat, IBGE, scientific repositories), and AI-assisted trend detection.

The NASA Farm Navigators challenge required turning raw satellite data into an educational, interactive farming simulation.

Created a Laravel + Vue platform powered by NASA POWER weather data to drive crop growth simulation, sustainability scoring, and learning feedback.

Laravel 12 + Vue 3 + Inertia.js with TypeScript and Tailwind, custom crop engine, and API-driven dashboards for climate trend analysis.

Legacy systems causing operational bottlenecks with 15% daily failure rate in SAP integrations affecting warehouse operations.

Architected a resilient middleware layer with circuit breakers, retry mechanisms, and real-time monitoring. Implemented event-driven architecture for async processing.

Chose Laravel over Node.js for the API layer due to mature ORM (Eloquent) for complex Oracle DB relationships, built-in queue workers for SAP jobs, and team expertise reducing onboarding time by 60%.

No visibility into production errors. Average incident detection time: 4+ hours. Customer complaints were the primary alert mechanism.

Deployed Sentry with custom dashboards, automated alert routing, and correlation analysis. Built internal tooling for incident management.

Selected Sentry over DataDog for cost efficiency (70% savings) while maintaining enterprise features. Custom Python scripts for advanced analytics not available out-of-box.

Executive reports required 8+ hours of manual data compilation weekly. High error rate in financial reconciliation.

Built automated ETL pipelines with Python, generating real-time dashboards and scheduled PDF reports with drill-down capabilities.

Python with Pandas over traditional BI tools for flexibility in custom calculations, easier integration with existing Oracle/PostgreSQL stack, and zero licensing costs.